用毫米波Wi-Fi识别走路姿势，准确率达91.2%
Beyond Sub-6 GHz: Leveraging mmWave Wi-Fi for Gait-Based Person Identification
- 用端到端深度学习分析毫米波与亚6吉赫兹频段信号
- 低采样率下对20人识别准确率达91.2%
- 首次在真实环境对比两类频段的识别性能
人员识别在实现智能、个性化和安全的人机交互中至关重要。近期研究证明可利用Wi-Fi信号通过个体独特的步态模式实现被动识别。尽管现有工作多集中于亚6吉赫兹频段，毫米波（mmWave）因具备更精细的空间分辨率，展现出新机遇，但其在人员识别中的相对优势尚未被探索。本文首次基于商用现成（COTS）Wi-Fi设备，在室内环境中构建了同步获取的双频段数据集，开展亚6吉赫兹与毫米波频段的比较研究。为确保公平对比，两个频段采用相同的训练流程与模型配置。借助端到端深度学习方法，我们发现即使在10 Hz低采样率下，结合有效背景抑制后，毫米波信号仍可实现91.2%的识别准确率（针对20名个体）。
原文摘要 · Abstract (English)
Person identification plays a vital role in enabling intelligent, personalized, and secure human-computer interaction. Recent research has demonstrated the feasibility of leveraging Wi-Fi signals for passive person identification using a person's unique gait pattern. Although most existing work focuses on sub-6 GHz frequencies, the emergence of mmWave offers new opportunities through its finer spatial resolution, though its comparative advantages for person identification remain unexplored. This work presents the first comparative study between sub-6 GHz and mmWave Wi-Fi signals for person identification with commercial off-the-shelf (COTS) Wi-Fi, using a novel dataset of synchronized measurements from the two frequency bands in an indoor environment. To ensure a fair comparison, we apply identical training pipelines and model configurations across both frequency bands. Leveraging end-to-end deep learning, we show that even at low sampling rates (10 Hz), mmWave Wi-Fi signals can achieve high identification accuracy (91.2% on 20 individuals) when combined with effective background subtraction.
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